1. Field of the Invention:
This invention relates to binaural hearing aids, and more particularly, to a noise reduction system for use in a binaural hearing aid.
2. Description of Prior Art:
Noise reduction, as applied to hearing aids, means the attenuation of undesired signals and the amplification of desired signals. Desired signals are usually speech that the hearing aid user is trying to understand. Undesired signals can be any sounds in the environment which interfere with the principal speaker. These undesired sounds can be other speakers, restaurant clatter, music, traffic noise, etc. There have been three main areas of research in noise reduction as applied to hearing aids: directional beamforming, spectral subtraction, pitch-based speech enhancement.
The purpose of beamforming in a hearing aid is to create an illusion of "tunnel hearing" in which the listener hears what he is looking at but does not hear sounds which are coming from other directions. If he looks in the direction of a desired sound--e.g., someone he is speaking to--then other distracting sounds--e.g., other speakers--will be attenuated. A beamformer then separates the desired "on-axis" (line of sight) target signal from the undesired "off-axis" jammer signals so that the target can be amplified while the jammer is attenuated.
Researchers have attempted to use beamforming to improve signal-to-noise ratio for hearing aids for a number of years {References 1, 2, 3, 7, 8, 9}. Three main approaches have been proposed. The simplest approach is to use purely analog delay and sum techniques {2}. A more sophisticated approach uses adaptive FIR filter techniques using algorithms, such as the Griffiths-Jim beamformer {1, 3}. These adaptive filter techniques require digital signal processing and were originally developed in the context of antenna array beamforming for radar applications {5}. Still another approach is motivated from a model of the human binaural hearing system {14, 15}. While the first two approaches are time domain approaches, this last approach is a frequency domain approach.
There have been a number of problems associated with all of these approaches to beamforming. The delay-and-sum and adaptive filter approaches have tended to break down in non-anechoic, reverberant listening situations: any real room will have so many acoustic reflections coming off walls and ceilings that the adaptive filters will be largely unable to distinguish between desired sounds coming from the front and undesired sounds coming from other directions. The delay-and-sum and adaptive filter techniques have also required a large (&gt;=8) number of microphone sensors to be effective. This has made it difficult to incorporate these systems into practical hearing aid packages. One package that has been proposed consists of a microphone array across the top of eyeglasses {2}.
The frequency domain approaches which have been proposed {7, 8, 9} have performed better than delay-and-sum or adaptive filter approaches in reverberant listening environments and function with only two microphones. The problems related to the previously-published frequency domain approaches have included unacceptably long input-to-output time delay, distortion of the desired signal, spatial aliasing at high frequencies, and some difficulty in reverberant environments (although less than for the adaptive filter case).
While beamforming uses directionality to separate desired signal from undesired signal, spectral subtraction makes assumptions about the differences in statistics of the undesired signal and the desired signal, and uses these differences to separate and attenuate the undesired signal. The undesired signal is assumed to be lower in amplitude then the desired signal and/or has a less time varying spectrum. If the spectrum is static compared to the desired signal (speech), then a long-term estimation of the spectrum will approximate the spectrum of the undesired signal. This spectrum can be attenuated. If the desired speech spectrum is most often greater in amplitude and/or uncorrelated with the undesired spectrum, then it will pass through the system relatively undistorted despite attenuation of the undesired spectrum. Examples of work in spectral subtraction include references {11, 12, 13}.
Pitch-based speech enhancement algorithms use the pitched nature of voiced speech to attempt to extract a voice which is embedded in noise. A pitch analysis is made on the noisy signal. If a strong pitch is detected, indicating strong voiced speech superimposed on the noise, then the pitch can be used to extract harmonics of the voiced speech, removing most of the uncorrelated noise components. Examples of work in pitch-based enhancement are references {17, 18}.